Optimal PV‐storage capacity planning for rail transit self‐consistent energy systems considering extreme weather conditions
Abstract With the rapid development of electrified rail transportation, the traction load demand of rail transportation has increased sharply, and its operational security under extreme conditions has been highlighted. Given the above background, this paper proposes a planning method for the optimal...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-12-01
|
| Series: | IET Renewable Power Generation |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/rpg2.12962 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832579839235719168 |
|---|---|
| author | Siyu Guo Liuyang Cai Haiyi Wu Guanghui Song Li Lin Yanbo Chen |
| author_facet | Siyu Guo Liuyang Cai Haiyi Wu Guanghui Song Li Lin Yanbo Chen |
| author_sort | Siyu Guo |
| collection | DOAJ |
| description | Abstract With the rapid development of electrified rail transportation, the traction load demand of rail transportation has increased sharply, and its operational security under extreme conditions has been highlighted. Given the above background, this paper proposes a planning method for the optimal photovoltaic (PV)‐storage capacity of rail transit self‐consistent energy systems considering the impact of extreme weather. First, the basic structure of a rail transit self‐consistent energy system is presented. Second, considering a power transmission system with line trip‐off under extreme weather conditions, a traction load reduction model is established to obtain the maximum power exchange capability between the power transmission network and rail substations. Subsequently, an optimal planning model for a hybrid energy storage system (HESS) is proposed to minimize the total HESS investment and rail transit system operation costs. Finally, the model is linearized as mixed‐integer linear programming and solved using Gurobi and the Yalmip toolbox. The simulation results verify the effectiveness of the proposed optimal PV‐storage capacity planning for rail transit self‐consistent energy systems. |
| format | Article |
| id | doaj-art-12a1047ae5064599a8d4b9d363db70e4 |
| institution | Kabale University |
| issn | 1752-1416 1752-1424 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Renewable Power Generation |
| spelling | doaj-art-12a1047ae5064599a8d4b9d363db70e42025-01-30T12:15:53ZengWileyIET Renewable Power Generation1752-14161752-14242024-12-0118163753376410.1049/rpg2.12962Optimal PV‐storage capacity planning for rail transit self‐consistent energy systems considering extreme weather conditionsSiyu Guo0Liuyang Cai1Haiyi Wu2Guanghui Song3Li Lin4Yanbo Chen5School of Electrical and Electronic Engineering North China Electric Power University Beijing ChinaSchool of Electrical and Electronic Engineering North China Electric Power University Beijing ChinaSchool of Electrical and Electronic Engineering North China Electric Power University Beijing ChinaSchool of Electrical and Electronic Engineering North China Electric Power University Beijing ChinaSchool of Electrical and Electronic Engineering North China Electric Power University Beijing ChinaSchool of Electrical and Electronic Engineering North China Electric Power University Beijing ChinaAbstract With the rapid development of electrified rail transportation, the traction load demand of rail transportation has increased sharply, and its operational security under extreme conditions has been highlighted. Given the above background, this paper proposes a planning method for the optimal photovoltaic (PV)‐storage capacity of rail transit self‐consistent energy systems considering the impact of extreme weather. First, the basic structure of a rail transit self‐consistent energy system is presented. Second, considering a power transmission system with line trip‐off under extreme weather conditions, a traction load reduction model is established to obtain the maximum power exchange capability between the power transmission network and rail substations. Subsequently, an optimal planning model for a hybrid energy storage system (HESS) is proposed to minimize the total HESS investment and rail transit system operation costs. Finally, the model is linearized as mixed‐integer linear programming and solved using Gurobi and the Yalmip toolbox. The simulation results verify the effectiveness of the proposed optimal PV‐storage capacity planning for rail transit self‐consistent energy systems.https://doi.org/10.1049/rpg2.12962distributed power generationenergy harvestingenergy storage |
| spellingShingle | Siyu Guo Liuyang Cai Haiyi Wu Guanghui Song Li Lin Yanbo Chen Optimal PV‐storage capacity planning for rail transit self‐consistent energy systems considering extreme weather conditions IET Renewable Power Generation distributed power generation energy harvesting energy storage |
| title | Optimal PV‐storage capacity planning for rail transit self‐consistent energy systems considering extreme weather conditions |
| title_full | Optimal PV‐storage capacity planning for rail transit self‐consistent energy systems considering extreme weather conditions |
| title_fullStr | Optimal PV‐storage capacity planning for rail transit self‐consistent energy systems considering extreme weather conditions |
| title_full_unstemmed | Optimal PV‐storage capacity planning for rail transit self‐consistent energy systems considering extreme weather conditions |
| title_short | Optimal PV‐storage capacity planning for rail transit self‐consistent energy systems considering extreme weather conditions |
| title_sort | optimal pv storage capacity planning for rail transit self consistent energy systems considering extreme weather conditions |
| topic | distributed power generation energy harvesting energy storage |
| url | https://doi.org/10.1049/rpg2.12962 |
| work_keys_str_mv | AT siyuguo optimalpvstoragecapacityplanningforrailtransitselfconsistentenergysystemsconsideringextremeweatherconditions AT liuyangcai optimalpvstoragecapacityplanningforrailtransitselfconsistentenergysystemsconsideringextremeweatherconditions AT haiyiwu optimalpvstoragecapacityplanningforrailtransitselfconsistentenergysystemsconsideringextremeweatherconditions AT guanghuisong optimalpvstoragecapacityplanningforrailtransitselfconsistentenergysystemsconsideringextremeweatherconditions AT lilin optimalpvstoragecapacityplanningforrailtransitselfconsistentenergysystemsconsideringextremeweatherconditions AT yanbochen optimalpvstoragecapacityplanningforrailtransitselfconsistentenergysystemsconsideringextremeweatherconditions |